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    Labour Market Adjustments and Migration in Europe and the United States: How Different?

    Robert C. M. Beyer and Frank Smets Goethe Universitt Frankfurt; European Central Bank and KU Leuven


    OCTOBER 2014


    Since the outbreak of the financial crisis in 2008, high and diverging unemployment

    rates across European countries and regions have become an increasingly important

    concern for European policy makers. In 2013 the unemployment rate in Spain was above

    25%, but only around 5% in Germany. Heterogeneity is large not only between countries

    but also within countries. For example, in France, Belgium and Spain the highest

    regional unemployment rates were twice as high as the lowest. In Italy, as an extreme

    example, the unemployment rate in Veneto was just a third of the unemployment rates in

    Campania or Sardinia. Moreover, this regional heterogeneity has increased since 2008

    (Marelli, Patuelli, and Signorelli, 2012).

    These persistent differences in unemployment rates across regions and countries have

    put the role of migration in labour market adjustment back on the European policy

    agenda. Migration can cushion the negative impact of adverse labour demand shocks on

    unemployment and thereby smooth the adjustment to heterogeneous macroeconomic

    developments. This is particularly important within a monetary union, in which relative

    wage adjustments may be slow due to the absence of nominal exchange rate adjustments.

    In 2013 the Commission adopted a proposal for a directive on new measures to facilitate

    labour mobility and the European Council agreed on measures to fight youth

    unemployment aiming, among other things, at increasing the mobility of young workers.

    The views expressed in this paper are our own and not necessarily those of the European Central Bank or its

    Governing Council. We are grateful to Nicola Fuchs-Schndeln, Michele Lenza, Giuseppe Bertola, Domenico Giannone, Ana Lamo, Jens Suedekum, four anonymous referees, to the participants of the 2013 ECB-CEPR

    conference on Heterogeneity in currency areas and macroeconomic policies at the European Central Bank,

    and to seminar and workshop participants at Goethe Universitt, DIW, and Deutsche Bundesbank for helpful comments.



    In this paper we contribute to this policy debate by empirically investigating how

    labour markets adjust to asymmetric labour demand developments and whether

    migration contributes substantially to this adjustment, using a modified version of the

    methodology of Blanchard and Katz (1992). In particular, we compare regional and

    country labour market adjustment in Europe with state adjustment in the US. The US is a

    natural benchmark for such a comparison because it is a large monetary union of similar

    size with a well-functioning, quite homogenous labour market. The US benchmark may

    therefore give an idea of how much scope there is for increased labour mobility and

    migration to play a role in labour market adjustment in Europe.

    We are not the first ones to make this comparison. In particular, Decressin and Fats

    (1995) and Obstfeld and Peri (1998) also applied the methodology of Blanchard and

    Katz (1992) to compare regional labour market adjustment in Europe and the United

    States.1 Overall, they found that the regional adjustment process is faster in the United

    States due to higher labour mobility. There are at least three reasons why it is important

    to update and refine this analysis.

    First, we have a much longer sample (38 years rather than 13 years in Decressin and

    Fats (1995)). This allows us to investigate the robustness of their findings and, more

    importantly, whether the adjustment process has changed over time. Since the early

    1990s European integration has continued to proceed in a number of areas which should

    facilitate the regional adjustment process. There is, for example, evidence that migration

    between European countries has increased due to the Schengen Agreement and the

    introduction of the euro (Beine et al., 2013). Some of these changes have become quite

    visible since the outbreak of the financial crisis. For example, net migration between

    Germany and the crisis countries (Spain, Portugal, Italy and Greece) has risen from

    minus 10.000 in 2009 to 70.000 in 2012. In contrast, interstate migration in the US has

    been decreasing since the 1980s and has dropped during the crisis to the lowest values

    since World War II (Frey, 2009). It is therefore interesting to see whether this has led to

    a convergence of the regional labour market adjustment process in Europe and the

    United States.

    Secondly, when comparing Europe and the United States, Decressin and Fats (1995)

    did not make a distinction between regional labour market adjustment within countries

    and between countries, while Obstfeld and Peri (1998) only focused on adjustment

    within countries. In this paper, we use the common factor methodology of Greenaway-

    McGrevy and Hood (2013) to filter out country factors and analyse the adjustment of

    countries to national labour demand shocks, which is likely to be hampered by bigger

    cultural, language and institutional differences. This allows us to investigate whether any

    convergence with the US is due to a smoother working of the adjustment process within

    or between countries.

    Thirdly, a straightforward comparison of the European and US results was hampered

    by the different data sources being used in those studies. We show that the differences

    are less pronounced when similar data sources are used.

    1 See Section 2 for a more detailed overview of the literature.



    The following findings are worth highlighting. First, looking at the full sample we find

    that both in Europe and the US labour mobility accounts for about 50% of the long run

    adjustment to region-specific labour demand shocks. The other 50% is accounted for by

    a reallocation of jobs across regions. But, in Europe it takes longer (10 years) than in the

    United States (5 years) for this adjustment process to be completed. And due to the

    greater rigidity of labour markets, the temporary response of the unemployment rate is

    more important and more persistent in European regional labour market adjustment.

    Second, we show that in Europe labour mobility is a less important adjustment

    mechanism in response to country-specific labour demand shocks. In this case, both the

    unemployment rate and the participation rate play a larger and more persistent role in the

    adjustment process. This underlines the remaining cultural, language and institutional

    barriers to labour mobility across European countries and provides support to European

    policy initiatives to further facilitate migration across countries.

    Third, in line with Dao, Furceri and Loungani (2014), we find that the role of

    migration in the regional adjustment process has decreased in the US. In contrast, in

    Europe migration has become a more powerful adjustment factor in response to both

    regional- and country-specific labour demand shocks in the second half of the sample

    (1990-2013 versus 1977-1999). This suggests that the acceleration of the European

    integration process after the early 1990s has led to more labour mobility across regions

    and countries.

    In the rest of the paper, we first briefly review in Section 2 how migration is typically

    analysed in the literature. Section 3, presenting the Blanchard-Katz methodology and our

    modifications, may be skipped by readers only interested in the results. The data is

    presented and discussed in Section 4 and Section 5 contains the main empirical analysis.

    Section 6 links our results to Blanchard and Katz (1992) and is not relevant for the main

    message. Finally, Section 7 discusses some policy implications.

    2. Studying Migration

    The importance of labour migration in facilitating adjustment to asymmetric shocks in

    a monetary union has been recognised at least since the seminal research on optimal

    currency areas of Mundell (1961). The empirical analysis of migration has, however,

    been hampered by the lack of reliable data. Recently an increasing number of papers

    have started to analyse migration patterns directly. Molloy, Smith and Wozniak (2011)

    analyse changes in the US over the last 30 years and detect a widespread decline in

    movements across all distances and across all population sub-groups. Frey (2009) shows

    that in 2007 migration rates in the US reached their lowest value since World War II and



    that the decline was strongest for interstate migration. Reasons for the decline in

    mobility remain, however, unclear.2

    Beine et al. (2013) with a new dataset containing 30 countries and covering the period

    1980-2010 come to contrary conclusions regarding migration in Europe. They find that

    both the Schengen Agreement and the introduction of the Euro have increased migration

    between the member countries. However, migration between countries covers only a

    small part of all movements. In Germany, for example, roughly twice as many people

    move every year within Germany from one state to another than from Germany to

    another country.

    Due to a lack of reliable data to analyse regional labour mobility directly, a large part

    of the literature has pursued the indirect approach proposed by Blanchard and Katz

    (1992). In their seminal paper on regional evolutions they develop a small model of

    regional labour markets (in the following: BK model) and suggest estimating the joint

    behaviour of the employment growth, the employment rate and the participations rate to

    analyse regional labour market adjustments to regional labour demand shocks. The

    respective reduced-form vector autoregression model (VAR) that they derive from their

    theoretical model offers an indirect approach to study migration because all employment

    changes unexplained by either the participation or the employment rate must originate

    from a change in population, which is identified with migration.

    Applying the methodology to US states, Blanchard and Katz (1992) find that as of the

    first year migration plays a dominant role in the adjustment process following a shock to

    regional labour demand. Decressin and Fats (1995) analyse large Western European

    regions and compare them to US states and find that in Europe the participation rate is

    the major force driving adjustment. Obstfeld and Peri (1998) analyse how regions in the

    US, Canada, the UK, Germany and Italy react to asymmetric labour demand shocks and

    show, first, that regional real exchange rates play a minor role in the regional adjustment

    process and, second, that the US adjustment process is the fastest due to higher labour


    The methodology of Blanchard and Katz (1992) has been applied in many other

    studies and has become the standard model to analyse regional labour market adjustment

    mechanisms and to approach migration patterns indirectly.3 Greenaway-McGrevy and

    Hood (2013) apply the model to metropolitan areas in the US and find that the

    adjustment to location-specific and aggregate shocks differ considerably. Our paper

    shares their main modification, namely the use of a factor structure to separate region-

    specific from common shocks. Dao, Furceri and Loungani (2014) reassess the

    2 Demographics and an aging of the population, increasing home ownership rates and an increasing share of

    women in the labour force may matter. Glaesser and Tobio (2007) discuss the role played by very long-term

    adjustment processes over many centuries that may have been concluded. Dao, Furceri and Loungani (2014)

    point to a decreasing dispersion of regional labour markets. Earlier papers detecting a decline include

    Greenwood (1997) and Long (1988). The recent decline in migration in the US may be somewhat

    overestimated (Kaplan and Schulhofer-Wohl, 2012). 3 Numerous other papers relied on the BK model: Jimeno and Bentotila (1998) adapt the methodology to study

    Spanish regions; Fredriksson (1999) looks at Swedish regions; Fidrmuc (2004), Gcs and Huber (2005),

    Bornhorst and Commander (2006) focus on regions in Central and Eastern Europe, and Tani (2003) suggests that migration in Europe is higher than expected.



    adjustment of US states and find that the contribution of migration has decreased since

    1980 and link it to a declining trend in the dispersion of unemployment rates across

    states. In addition, they show that migration contributes more in aggregate downturns

    and sketch some differences between the US and Europe.

    For our purposes there is no alternative to inferring migration indirectly as in the

    Blanchard and Katz (1992) methodology. But we acknowledge that the chosen approach

    comes with drawbacks, including weak micro-foundations and a debatable identification

    of the labour demand shocks. Due to the availability of more and better regional data

    economic geography offers an increasingly feasible alternative. Counterfactual analyses

    in spatial general equilibrium models as in Redding (2012), Ahlfeld et al. (2013), or

    Behrens et al. (2013) could be used to understand how individuals relocate after a

    shock and where they move. An alternative approach is to look at how mobility response

    to well-identified shocks. Both in the US and Germany trade shocks, for example, have

    been shown to induce relatively small mobility responses (Autor, Dorn, and Hanson

    2013; Dauth, Findeisen, and Sdekum 2014).


    3.1 Intuition of the BK Model

    In this section, we provide some intuition behind the BK model. For a full model

    description, we refer the interested reader to the original paper of Blanchard and Katz

    (1992). Starting from the observation that region-specific labour demand shocks have

    permanent effects on employment, but only temporary effects on the employment rate,

    the participation rate and wages, Blanchard and Katz (1992) develop a simple model of

    regional labour market dynamics that is based on two basic features. First, regions are

    assumed to produce distinct bundles of goods that are sold in an aggregate goods market

    and, second, labour and capital are assumed to be perfectly mobile in the long run. In

    this model, state-specific shocks to labour demand result in short-lived mean deviations

    of wages, but cause permanent changes of the employment level. An adverse shock to

    labour demand, for example, increases unemployment and lowers wages, which induces

    some workers to leave the region. Since workers move out of the region until wages are

    back to equilibrium, lost jobs after an adverse demand shock are not fully recovered.

    Similarly, when region-specific labour demand increases, relative wages tend to

    increase. Thus leads some firms to relocate at least part of their production outside the

    region and thus reduces employment compensating for some of the newly created jobs.

    However, higher wages also cause inward migration of workers so that some of the

    newly created jobs remain permanently in the region. The relative sensitivities of labour

    demand and supply determine how large the permanent effect of the labour demand

    shock is on regional employment. In the short run, changes in the unemployment and the

    participation rate can also contribute to the change in employment.



    In order to implement this model empirically and in the absence of reliable regional

    wage data, Blanchard and Katz (1992) propose to estimate the joint behaviour of

    employment growth, the employment rate and the participation rate. The short and long

    run adjustment of the regional labour market can then be analysed by tracing out the

    impact of a shock to the employment growth equation.

    3.2 Region-Specific Variables

    Blanchard and Katz (1992) measure region-specific variables as simple differences

    between the regional variables and their aggregate continental counterpart. Let stand

    for the number of persons employed, for the labour force in persons and for the

    population in persons, in region i, at time t; let

    contain the regional employment growth, employment rate and participation rate;

    and let stand for the respective continental data. Then the region-specific variables

    denoted by are given by


    This definition of a region-specific variable boils down to conditioning each of the

    variables on one common factor (the continental aggregate variable) and to restricting

    the loading on that factor to be equal to one.4 Such a transformation will identify the

    adjustment to region-specific shocks, only if all regions respond identically to aggregate

    fluctuations. But in a regression of regional variables on their aggregate counterparts

    most coefficients are quite different from one, suggesting that regions react quite

    heterogeneously to aggregate business cycles (see Hamilton and Owyang 2012).5 In this

    case, the simple transformation like in equation (1) will estimate a mixture of the

    adjustment to local and aggregate shocks. One advantage of the simple difference

    transformation is that one does not need to identify local and aggregate shocks. This may

    still be justified if the regional dynamics is independent of the local or aggregate origin

    of the shock.

    There may, however, be reasons why regions adjust differently following aggregate

    versus idiosyncratic shocks. For example, using the BK methodology Dao, Furceri and

    Loungani (2014) find that the regional adjustment differs depending on aggregate

    conditions. One explanation may be that job-churnings are pro-cyclical, i.e. they

    decrease during an economic bust and increase in good times (Fallick and Fleischman

    4 For large cross-sections the idiosyncratic components average out so that the aggregate converges to the common factor (Forni and Reichlin 1998 and Pesaran 2006). For a large sample this is hence identical to

    including a common time trend. The aggregate most often refers to national variables (as in Blanchard and

    Katz 1992 or in Obstfeld and Peri 1998) but continental variables can also be used (as in Decressin and Fats

    1995). 5 Decressin and Fats (1995) reject a unity reaction of regions to aggregate shocks for most regions as well.

    They suggest using the estimated coefficients as weights when differencing, so that regions are allowed to react with a different sign and magnitude to aggregate movements. They thus condition on one common factor per

    variable, but allow for different weights. These variables, so called -differences, are uncorrelated with

    aggregate variables and, if there were only one common factor per variable, would indeed enable a separation of regional and aggregate fluctuations.



    2004; Caballero and Hammour 2005; Molloy, Smith and Wozniak 2011; Davis et al.

    2011). As a result, when a region is hit by an idiosyncratic negative labour demand

    shock and the labour market in other regions is not affected, it may be easier to find a job

    there and the incentive to migrate may be higher. In contrast, when the whole country is

    negatively affected but one region worse than another, it may be more difficult to find a

    job in the region that is hit less, dampening the incentives for migration.

    Greenaway-McGrevy and Hood (2013) show how a factor model can be embedded

    into the structural innovations of the original BK model in order to distinguish between

    the adjustment to aggregate and local shocks. Region-specific variables are then defined

    as residuals of a factor model:



    where , , are the factors and

    , , are constant but region-

    specific loadings.

    Intuitively, regions are allowed to respond to two different processes, namely a

    local, idiosyncratic shock process and a set of common or aggregate shock processes,

    with potentially different responses. The data is modelled as the sum of these two

    processes. Strong-form dependence in the panel allows consistent identification of the

    factors justifying their use in linear regressions (Bai and Ng 2006, Bai 2009,

    Greenaway-McGrevy and Hood 2013). Greenaway-McGrevy and Hood (2013) show

    that the adjustment processes of MSAs are different after location-specific and

    aggregate shocks. In the former case migration is rapid but relatively weak. Conversely,

    the adjustment after common shocks is driven by more prolonged and larger migration.

    3.3 Estimation Procedure

    Partly following Greenaway-McGrevy and Hood (2013), our estimation proceeds in

    two steps.6 In the first step, we decompose the regional variables in three orthogonal

    components: the contribution of a continent-wide factor, of a country factor and a

    region-specific variable. This is done by estimating a multi-level factor model. In the

    second step, we separately estimate a pooled VAR in the region-specific variables and

    the country factors to investigate and compare the labour market response to region-

    specific7 and country-specific shocks respectively.

    3.3.1 The Factor Model

    We estimate a separate multi-level factor model for Europe and the US. We include

    one continental factor, one country factor in Europe and one area factor in the US. In

    Europe, we include a German (G), French (F), Italian (I), Spanish (SP) and British (GB)

    6 Because in this model also the data vector follows a factor structure the factor model can be estimated before

    the VAR. For more details regarding the augmented BK model refer to Greenaway-McGrevy and Hood

    (2013). 7 We use the terms region-specific, idiosyncratic and local shock interchangeably.



    factor, and in the US we include the four US areas Northeast (NE), Midwest (MW),

    South (S), and West (W).8 We restrict the loadings so that only regions belonging to a

    particular country (area) are able to load on the respective country (area) factor.9

    Accordingly, the following factor model is estimated for Europe and the United States



    Where i denotes the region, c the country in Europe the region belongs to or the area the

    state in the US belongs to, and a is the continent (Europe or US). The idiosyncratic

    component contains the region-specific variables. The loadings represent the

    sensitivity of the regional series to the country, area or continental factors and since they

    are region-specific, they allow for heterogeneous effects of those factors.

    Since principal-components methods cannot account for a hierarchical factor structure,

    we estimate the factors with the quasi-maximum likelihood approach of Doz, Giannone,

    and Reichlin (2012). They show that maximum likelihood is suitable to estimate the

    common factors in large cross-sections of time series. We implement the QML estimator

    using the Kalman smoother and the EM algorithm.10

    3.3.2 The Vector Autoregression Model

    We then separately estimate the following panel VAR and pool over different




    8 Different factor structures are, of course, possible. The results are not changing importantly for different

    structures. 9 We impose a structure on the factors in order to capture the variables pervasive covariation for the different geographical entities. In Europe it is important to account for country factors. Using the ABC criterion of

    Alessi, Barigozzi and Capasso (2010), we find indeed strong evidence for more than one common factor per

    series. 10 Forni, Hallin, Lippi, and Reichlin (2000) and Stock and Watson (2002) propose to estimate common factors

    using principal components. Principal components are indeed easy to compute and consistent for any path of

    the cross-section and sample length (Bai and Ng 2002; Forni, Giannone, Lippi, and Reichlin, 2009). Yet, with principal components it is not possible to restrict the factor structure as we intend. Other authors working with

    structural factors include: Forni and Reichlin (2001); Bernanke, Boivin, and Eliasz (2005); and Boivin and

    Giannoni (2006). Also Kose, Otrok, and Whiteman (2003) apply a likelihood based estimator. The QML

    approach of Doz, Giannone, and Reichlin (2012) assumes that all series are I(0). In our case, however, some

    series are I(1). Principal components deliver consistent estimates also in this case (Bai and Ng 2004). We re-

    estimate the three global factors using principal components and the structural factors of the remaining unexplained fluctuations that all turn out to be I(0) with the QML approach. The factors are very similar.

    Doing the factor analysis in two steps underestimates the errors, because the QML estimation uses estimated

    data. However, in the VAR we treat the factors in any case as observations (Bai 2003, Giannone and Lenza 2009).



    where the region- or country-specific constants represent regional or country fixed

    effects that allow for different long-term averages.11

    Given our large cross-section and

    modest sample length the two-step procedure does not cause a generated regressor

    problem (Pagan 1984, Bernanke and Boivin 2003, Bai and Ng 2006) so that we can

    indeed treat the region- and country-specific variables as observations (Bai 2003,

    Giannone and Lenza 2009).

    The short and long run adjustment of the regional labour market can then be analysed

    by tracing out the impact of a shock to the employment growth equation on the other

    variables. The identifying assumption is that this shock captures unexpected changes in

    regional labour demand meaning that contemporaneous employment growth is weakly

    exogenous in the other equations of the VAR. The Choleski decomposition implies that

    current changes in employment affect both employment and participation rates but not

    vice versa. There are examples that violate this assumption, for example changing

    fertility rates, but we assume these changes are small relative to the labour demand


    A region-specific labour demand shock is a change in labour demand in a region that is

    uncorrelated with national and continental labour demand. Think for example of a

    change in local government spending, the bankruptcy of a big company with many

    employees in one particular region, or a regional natural catastrophe like a storm tide.

    Examples of shocks to country-specific labour demand could result from a change in

    military spending, oil prices, a national banking crisis or changes in national policies.

    Note that



    Changes of the employment level thus stem either from changes of the employment

    rate, the participation rate or the population. With the VAR we can distil the population

    response, since any change that is not explained by the employment rate or the

    participation rates is attributed to a change of the population. Following Blanchard and

    Katz (1992), we will assume that these changes of the population are due to migration.

    11 We could also estimate (4) using the original regional variables on the left-hand side and augmenting the

    VAR with the continental and country (area) factors. Results are very similar. 12 Dao, Furceri and Loungani (2014) in a recent working paper test the assumption for the US and conclude that identification with an instrument reveals a lower contribution of migration. We are not fully convinced that

    the only effect of the IV identification is a clearer demand shock, as it may also change the type of the

    adjustment. Because the IV approach is very difficult to implement in Europe also Dao, Furceri and Loungani (2014) rely on our assumption for their European analysis.




    4.1 Regional Disaggregation and Data Sources

    The regional disaggregation follows Blanchard and Katz (1992) for the US and is

    similar to Decressin and Fats (1995) for Europe. For the US, the disaggregation is

    straightforward: we count each state plus the District of Columbia as a region so that

    there is a total of 51 US regions. In Europe entities of comparable size refer less strictly

    to administrative divisions. Yet, all regions in the sample can be understood as

    consisting of one or more NUTS2-regions. We include eight French, seven German,

    eleven Italian, seven Spanish, and eight British regions, as well as Belgium, Denmark,

    Greece, Ireland, the Netherlands and Portugal. While Decressin and Fats (1995) classify

    the small countries as regions, they are treated as countries in our set-up. For a list of all

    regions see Appendix A.

    We use data on the population, labour force and employment, from which we compute

    the employment growth, the (un)employment rate, as well as the participation rate. Our

    time series starts in 1976 and ends in 2013 so that it covers 38 years. The primary

    European data sources are the national Labour Force Surveys. We apply some data

    modifications to fill in missing data points and replace data of obviously bad quality

    using data from different international and national sources. The data from different

    sources is linked using adjusted growth rates of the working-age population, the

    unemployment and the participation rates. They are then used to extend the most recent

    data backwards. We compared different ways to link the data and found that differences

    are minor. For European regions we restrict the sample to the working-age population so

    that all series cover only persons between 15 and 64 years old.

    For the US we use the Current Population Survey (CPS) as our main data source

    because it is comparable to the European Labour Force Surveys. In section 6 below, we

    also use Local Area Unemployment Statistics (LAUS) from the Bureau of Labor

    Statistics as an alternative data source for investigating the US adjustment mechanism

    because these are establishment data that are closer to the data used by Blanchard and

    Katz (1992). All US series include all persons older than 15 years.

    For more details regarding the regional disaggregation as well as data sources and

    modifications refer to the data appendix.

    4.2 Descriptive Statistics

    In 2013 the average regional population in the US was 4.8 million with a standard

    deviation of 5.4 million leading to a coefficient of variation of 1.1. With 30 million

    California was the biggest region in the US and with less than half a million Wyoming

    was the smallest. The average regional working-age population in Europe is very similar

    and equal to 4.6 million but the standard deviation is with 2.4 million smaller, resulting

    in a smaller coefficient of variation, 0.5. Nordrhein-Westfalen in Germany is the largest

    region with a working-age population of 12 million in 2013, whereas Abruzzi-Molise in



    Italy is the smallest with only 1 million inhabitants. The total working-age population in

    2013 was 240 million in the US and 220 million in Europe.

    The average unemployment rate in a US region in 2013 was 6.8% with a standard

    deviation of 1.6%. In Europe the average unemployment rate was nearly twice as high,

    namely 12.5%, and the regions were much more heterogeneous, as indicated by a

    standard deviation of 7.9%. Over the whole sample the average unemployment rate was

    6% in the US and 10% in Europe.

    [Insert Figure1 here]

    Figure 1 plots the continental means of employment growth, the unemployment rate

    and the participation rate over the period 1977 till 2013 in the US and Europe.

    Employment growth fluctuates strongly, in particular in the US. While employment

    growth was on average higher in the US than in Europe in the earlier part of the sample,

    growth rates have become more similar since then. The unemployment rate shown in the

    middle panel is less volatile and returns to its mean roughly every ten years. During most

    of the sample the unemployment rate is higher in Europe than in the United States.

    Finally, the lower panel shows the participation rate, noting that for Europe this only

    includes persons below the age of 64. The participation rate in Europe shows a clear

    upward trend throughout the sample, whereas in the US the participation rate increased

    until 2000, and started to decline afterwards.

    [Insert Figure 2 here]

    Figure 2 plots the standard deviation of regional unemployment rates over time. In

    Europe regions diverged until 1998. Following the introduction of the euro in 1999 they

    converged very fast.13

    However, since 2008 regional unemployment rates are again

    diverging strongly in Europe. As a result, in 2013 the dispersion reached its maximum

    over the sample period. In contrast, regional unemployment dispersion is considerably

    lower in the US than in Europe, confirming that US regions are more homogenous than

    European ones. Also note that in the US regions diverge particularly in recessions: the

    three steepest increases of the standard deviation in the early eighties, the early

    nineties, and between 2008 and 2010 all coincide with recessions.14

    13 In the same period the standard deviation of unemployment rates of other developed countries decreased as

    well, but less than in Europe (Estrada, Gal and Lpez-Salido, 2013). 14 The connection between increasing standard deviations and recessions is also discussed in Greenaway-McGrevy and Hood (2013) as well as in Dao, Furceri and Loungani (2014).



    4.3 Variance Decomposition

    Next we estimate the multi-level factor model (3) to extract the common factors from

    the data.

    [Insert Table 1 here]

    Table 1 reports the proportion of variance explained by each level for each variable.

    The common European factor explains 28% of the employment growth fluctuations,

    41% of fluctuations in the employment rate and 69% of fluctuations in the participation

    rate. Country factors are nearly as important for the first two series, but matter less for

    changes in the participation rate. The importance of the country factors in Europe

    supports our strategy to estimate a multi-level factor model. Together the EU and

    country factors capture between 57% of the variance in employment growth and 85% of

    the variance in the participation rate. Idiosyncratic fluctuations are most important (43%)

    for the employment growth rate.

    The greater homogeneity of the US economy is reflected in the fact that the US factor

    plays a more important role in accounting for both employment growth and employment

    rate fluctuations. As expected, US states are thus more correlated and their business

    cycles more aligned than regions in Europe. The area factors, on the other hand, explain

    less than half of the variance that is captured by the country factors, clearly showing that

    country factors are more important in Europe. The contributions of region-specific

    shocks are similar to the ones in Europe with a slightly lower contribution for the

    employment rate.


    In this section, we compare the labour market adjustment of regions to region-specific

    shocks in Europe and the US, and analyse as well the country adjustment in Europe.

    Moreover, we analyse changes in the role of labour mobility over time.

    In each case, the figures below report impulse responses of the employment level, the

    employment rate and the participation rate to a positive one standard deviation shock to

    labour demand. Note that deviations of the employment rate are approximately equal to

    negative deviations of the unemployment rate. The responses show percentage

    deviations from region-specific means. In addition, we include a table below the impulse

    responses that shows the adjustment in the first five years and in the long run to a

    normalised initial increase of 100 jobs. Each table reports in the first line the number of

    newly created jobs and in the lines below it decomposes the new jobs. Some of the new

    jobs are filled with formerly unemployed, others with people previously not forming part

    of the labour force and the remaining jobs are filled with people moving into the region.



    5.1 Regional Adjustment to Region-Specific Shocks

    First we discuss the adjustment of regions to region-specific changes in labour demand

    and compare the adjustment in Europe and the US. We estimate (4) and allow for two


    We test for unit roots and confirm that all series are stationary so that the model

    specification is appropriate.16

    [Insert Figure 3 here]

    Figure 3 shows the impulse responses for Europe in the left and for the US in the right

    panel. Note, first, that following a positive labour demand shock the employment level

    increases on impact, then falls back towards its initial level, but remains above it in the

    long run. The fact that some but not all of the initial increase in employment remains in

    the long run suggests that both labour migration and job destruction or migration play a

    role in the adjustment process. If no jobs disappeared, the permanent effect would be the

    size of the initial increase. If, on the contrary, no migrants were moving into the region,

    the permanent effect on employment would be zero. Since in the long run the

    unemployment and participation rates revert to their pre-shock baseline, the permanent

    change in employment must stem from migration. The permanent change in employment

    relative to the initial increase thus reveals the relative importance of job migration versus

    migration of employees. Due to the normalization the number of workers migrating in

    the long run reported in the tables can be interpreted as the long-run contribution of

    migration as percentage of the initial increase in employment.

    A number of points are worth making. First, the adjustment towards the new steady

    state is faster in the US than in Europe. Employment reaches its long run level after 10

    years in Europe and after five years in the US. After three years both the employment

    and participation rate continue to contribute substantially in Europe, but not in the US.

    After four years they still contribute more than 20 per cent in Europe, but only five in the

    US. The employment rate (or unemployment rate) reacts much stronger in Europe and

    contributes a lot more to the adjustment than in the US. Migration, on the other hand,

    contributes a bit less in Europe over the whole adjustment period. Overall, a shock

    changing employment initially by 100 workers leads to 47 immigrants in Europe and 57

    in the US. In other words, due to migration 48% of the initial increase of employment

    becomes permanent in Europe and 57% remain in the US. While migration is higher in

    the US, the differences are not large.

    Summarizing, there are differences between the regional adjustment mechanisms in

    Europe and the US in Europe it is more persistent, employment rates contribute more,

    and migration less but the differences are smaller than previous work suggests.

    Compared to Decressin and Fats (1995), we find a faster adjustment mechanism, a

    15 Two lags are usually used in the literature. We estimate the model also with only one and four lags and find

    that the results are very similar. 16 We use the panel unit root test of Harris and Tzvalis (1999) and reject a unit root for all series at the 1% level.



    more important role for job creation (and consequently a less important role for

    migration), and smaller differences between Europe and the US.

    5.2 The National Adjustment Mechanisms in Europe

    Next we investigate the role of migration in labour market adjustment across countries.

    The costs of migrating across countries are likely to be higher than those of migrating

    between regions due to the larger distance, greater language and other cultural barriers,

    and other institutional obstacles like the limited portability of pension and other social

    security rights. We should therefore expect a lower contribution of migration to the

    adjustment process following country-specific labour demand shocks.

    We use the five country factors from (3) and add our small countries so that we have a

    cross-section of 11 countries. We estimate (5) and due to the smaller cross-section now

    allow for only one lag. Again we confirm the empirical validity of the VAR


    [Insert Figure 4 Here]

    The left panel in Figure 4 shows the impulses responses of a one standard error

    positive labour demand shock as before. Note that the standard errors are now larger as

    the cross-section is smaller. The employment and participation rate contribute nearly

    equally in all years and need 15 years to return to their pre-shock level. As a result, the

    adjustment process takes longer in response to country-specific shocks than in response

    to region-specific shocks. The right panel compares the number of migrants in the first

    five years after an initial employment change of 100 workers for the different adjustment

    mechanisms. From before we know that the number of migrants is somewhat lower after

    a region-specific shock in Europe compared to the US. Migration is much lower after a

    country-specific shock, in particular in the first years after the shock. In the first year

    only 18 workers migrate to a country experiencing an unexpected increase of the

    employment level by 100 jobs, whereas around 40 workers migrate after a region-

    specific shock of that size in Europe and the US. These differences become smaller over

    time. Migration also contributes less to the change in employment relative to the

    participation and employment rate. In the first three years it contributes on average 51%

    to the regional employment change in Europe after a local shock but only 21% to the

    national adjustment after a country shock.18

    Summarizing, we find that migration plays a less important role in the adjustment to

    country-specific shocks. Since in section 5.1 we found that the regional adjustment

    processes in Europe and the US are not very different, it follows that it is mostly lower

    17 Here we test for unit roots using the test developed by Levin, Lin and Chu (2002). A unit root is rejected at

    the 1% level for the employment growth, the participation rate, and for the employment rate. 18 We have also estimated the national adjustment mechanism with the country series instead of the factors. Results are very similar.



    labour mobility between European countries that slows down adjustment in Europe and

    may contribute to the large heterogeneity in labour market pointed out in the


    5.3 Changes over time

    In the previous sections we reported the full-sample results. Given the evidence of

    changes in labour mobility discussed in the introduction, in this section we analyse

    whether the role of migration has changed over time.

    To do so, we estimate the VARs of equations (4) and (5) for two subsamples

    separately (1977-1999 and 1990-2013). While this obviously shortens the sample, we

    still have 23 observations per subsample and thus nearly twice as many observations as

    Blanchard and Katz (1992) and Decressin and Fats (1995). Still, we reduce the lag

    length to one and focus mainly on the first five years in order to minimize issues related

    to sample length. Note that our samples overlap so that changes originate in differences

    in the adjustment in the first and last 13 years.

    [Insert Figure 5 here]

    Figure 5 shows the changes of the regional migration response in Europe and the US,

    as well as the national migration response in Europe. The left panel plots the total

    number of migrants after a shock of 100 workers in the first five years. The dashed lines

    show the numbers of migrants between 1977 and 1999 and the solid lines the numbers

    between 1990 and 2013. In addition, we use pie charts to report the average percentage

    contributions of the employment and participation rate and of migration to the

    employment change in the first three years. This allows us to see whether migration has

    become relatively more important or not.

    The upper panel reports the changes in the regional adjustment in Europe. The total

    number of migrants has risen in all years and also the percentage contribution of

    migration has increased. Molloy, Smith and Wozniak (2011) analyse inter-NUTS2

    mobility in Europe using a LFS question asking whether respondents moved in the

    previous year. In line with our results, they find that mobility rates were either flat or

    slightly increasing in the early 2000s.

    The increase of migration in Europe detected by Beine et al. (2013) refers to migration

    between countries and not regions. As discussed in the introduction, recent divergence in

    unemployment rates across European countries has led to increased migration in Europe.

    It is thus interesting to see whether we can also detect changes in the adjustment to

    country shocks using our methodology. The middle panel of Figure 4 shows the changes

    in the country adjustment mechanism. As expected, the total number of migrants in

    response to an initial increase in employment of 100 has indeed increased. After three

    years, for example, it decreased from 31 in the first subsample to 45 in the second

    subsample. And also the permanent effect of a country shock on migration has become

    more important. Although not directly comparable, our results therefore qualitatively



    confirm the findings of Beine et al. (2013). In sum, we find that in the most recent

    subsample country-specific changes in labour demand set in motion more cross-country

    movement in workers and that this migration contributes more relative to the

    employment and participation rate. At the same time, the role of migration between

    countries remains lower than its role between regions.

    Finally, the lower panel shows changes in the role of migration in the US. The total

    number of migrants after a region-specific shock has notably decreased in all years.

    Three years after the shock the number of migrants has decreased from 56 to 44. As the

    pie charts show, the percentage contribution has declined as well and is compensated by

    a more flexible labour force. For the US our results are thus in line with Dao, Furceri and

    Ploungani (2014) and relate nicely to the literature on declining labour mobility in the


    6 Relation to Blanchard and Katz (1992)

    In this section, we apply the original methodology of Blanchard and Katz (1992) who

    defined regional variables as simple differences from the continent-wide mean to our

    data. This is useful for two reasons. First, our results differ quite importantly from those

    of Blanchard and Katz (1992) and Decressin and Fats (1995) who found a much slower

    adjustment process and a greater role for migration. In this section, we want to

    investigate whether these differences are mainly due to the change in methodology or

    also due to use of different data sets. Second, one might argue that the policy maker is

    interested in the regional adjustment to differences independent of the type of the shock.

    This may be captured somewhat better by analysing simple mean differences.

    6.1 The adjustment with simple differences

    [Insert Figure 6 here]

    Figure 6 plots the impulse response functions for Europe and the US using simple

    differences computed as specified in (1). While this specification results in stationary

    series in the US, in Europe we can reject a unit root neither in the employment rate nor

    in the participation rate so that that this filtering strategy is not appropriate for European


    As discussed before, our factor-based methodology of identifying region-

    specific variables results in stationary series.

    In Europe, the employment level exhibits a hump-shaped response and migration is

    initially lower than for region-specific shocks. The number of migrants in the first years

    drops from 39 to 25, but is nearly identical in the long run (47 versus 46). The

    19 With Harris-Tzvalis test we reject a unit root in the US for all series at the 1% level. In Europe only the

    employment growth is stationary we reject a unit root at the 1% level but both for the employment and participation rate we cannot reject the unit root at any level.



    participation rate is now much more persistent and is considerably above the pre-shock

    level even 20 years after the shock. The employment rate contributes stronger and is

    more persistent as well.20

    Overall, it looks like the original BK methodology mixes the adjustment to region-

    specific shocks with the adjustment to country-specific shocks. This results in a more

    persistent adjustment process with a larger role for unemployment and a significantly

    smaller role for migration.

    Accordingly, in the US the differences are smaller and the responses look generally

    similar to the ones after region-specific shocks. But again the process now takes longer

    to be completed and in particular the contribution of the participation rate is more

    persistent. Using simple differences, migration is a little lower initially, in the first year

    we see 37 instead of 43 migrants, and a little higher in the long run with 63 instead of 57

    migrants. The general conclusions from Section 5.1 are thus confirmed.

    Next, we repeat the estimation for the same subsamples as before with simple

    differences. Figure 7 reports again the number of migrants after a shock of 100 workers

    in the left panel and the average percentage contributions in the first three years in the

    right panel.

    [Insert Figure 7 here]

    From 1977-1999 to 1990-2013 the total number of migrants has again gone up in

    Europe, though only from the third year onwards. The average percentage contribution

    in the first three years is nearly the same but would increase if we added more years.

    As before, the number of migrants has clearly decreased in the US and also the

    percentage contribution in the first three years has gone down. Our results from Section

    5.3 are thus also confirmed.

    6.2 Local-Area Unemployment Statistics

    While using simple differences brings the US impulse responses closer to the ones in

    Blanchard and Katz (1992), we still neither observe the strong hump-shaped response

    that characterises their responses nor the related permanent effect on migration of around

    100%. In this section, we analyse whether the different data source may be the reason for

    this. We estimate the adjustment process (4) for the US using simple differences and the

    LAUS data set, which is establishment data closer to the data used by Blanchard and

    Katz (1992). Figure 8 shows the impulse responses to a positive one standard deviation


    [Insert Figure 8 here]

    20 We also estimated the regional adjustment with -differences (see footnote 5) and find very similar results. 21 The Harris-Tzvalis test rejects a unit root at 1% for employment growth and the employment rate and at 5%

    for the participation rate.



    In this case, the impulse responses look very similar to the responses reported in

    Blanchard and Katz (1992) and more recently in Dao, Furceri and Loungani (2014).

    Above all, the impulse response now is strongly hump-shaped and migration is more

    than twice as important in the long run and above 100%.

    We can only speculate about the reasons for the large differences with our results and

    the larger contribution of migration in the long run. Since migration is identified as the

    residual of the VAR, i.e. migration is given by the change of the employment level that

    cannot be explained by changes in either the employment or the participation rate, the

    quality of the data series may be very important. Inconsistent data series may result in a

    larger contribution of the residual and hence of migration. Employment data from LAUS

    is based on establishment data and there are important differences between household

    and establishment series resulting from different definitions, coverages, and estimation

    procedures. For example, CPS employment includes self-employed persons, unpaid

    workers in family-operated businesses, and agricultural workers; establishment-based

    employment data from the Current Employment Statistics does not. Unpaid absences

    from work are differently accounted for and persons working in more than one

    establishment are counted more than once with establishment-based data. The latter

    inconsistency clearly matters: Blanchard and Katz (1992) overestimate migration

    because they rely on establishment-based employment data, but on CPS data for

    unemployment and persons out of labour force so that some of the unexplained

    employment changes may result from changes in dual job holding and not migration.

    With LAUS data the same might happen.


    7.1 Summary

    In this paper we revisit the role of labour mobility in regional labour market

    adjustments in Europe and the US. We study 41 European and 51 US regions over a

    period of 38 years. In line with Greenway-McGrevy and Hood (2013), we use a factor

    model to distinguish between the regional adjustment to region-specific idiosyncratic

    shocks and the country adjustment to country-specific shocks. We show that

    distinguishing between whether migration takes place between regions or between

    countries matters for the relative importance of both migration and unemployment.

    In particular, we find that, once we control for country factors, the regional adjustment

    process in Europe is not that different from the one in the United States. In both areas,

    migration plays a relatively important role in the long run, but in European countries the

    adjustment process takes somewhat longer and is accompanied by larger changes in

    unemployment reflecting more rigid labour markets.

    What makes a difference is the cross-country adjustment process in Europe. Due to

    remaining differences in language, cultural factors and institutional differences, the role



    of migration is much less important when a country is hit by a labour demand shock. At

    the same time, changes in the employment rate are more important reflecting different

    national labour market institutions. If one does not account for the country factors, the

    differences in regional adjustment between Europe and the US become much larger.

    Using a much longer data set, we also find that the adjustment processes in Europe and

    the US have further converged over the past decades. This reflects both a fall in

    interstate migration in the US and a rise in the role of migration in Europe as European

    integration proceeds. The latter shows up most strikingly in an increased role of

    migration in the cross-country adjustment.

    Finally, we show that part of the difference between Europe and the US in previous

    studies may in addition be due to the use of different data sources.

    7.2 Policy Implications

    Our findings can inform the policy debate in at least two dimensions. First, most of the

    differences in the role of migration in the regional labour market adjustment process

    between the US and Europe are due to remaining barriers connected with country

    borders. It is therefore right for European policy makers to focus on how to facilitate

    labour mobility across countries in Europe. Our empirical investigation shows that

    measures taken in the past such as the Schengen agreement, initiatives to bring down

    cultural barriers through exchange programmes such as the Erasmus programme or

    efforts like the Bologna process to harmonize educational standards may already have

    contributed to a greater role for labour mobility in labour market adjustment. And there

    is scope for additional measures to further reduce the persistence of labour market

    adjustment to country-specific shocks and alleviate the associated social costs. A

    variety of measures can be considered including promoting more flexible housing

    markets, increasing the compatibility of school systems, improving language education,

    harmonizing pension systems and promoting the portability of pension and other social

    security rights, and changing the general attitude towards migrants. The recent

    initiatives of the European Commission and Council may hence help to foster

    adjustment to country-specific shocks.

    However, our analysis also reveals that the differences with the United States, a

    monetary union with a quite homogenous culture and a well-functioning labour market,

    are not that large. Given that cultural and language barriers are likely to persist in

    Europe, it is therefore important to be realistic about what increased labour mobility can

    achieve. The differences in the importance of migration in Europe and the US are

    smaller than has previously been argued, so that labour mobility might not hamper the

    functioning of the Euro Area as strongly as some argue.

    To become more specific is difficult given the positive nature of our analysis. This

    would require a more structural and normative approach. In this context, one should

    also recall that there are also costs to migration, in particular when it involves high-

    skilled migration that may tend to exacerbate rather than alleviate regional disparities.



    Moreover, large-scale migration in Europe could be socially disruptive (Emerson et al.,

    1992; Obstfeld and Peri, 1998). Moreover, from a normative perspective it is not clear

    whether adjustment through workers or jobs is preferable. An acceleration of the labour

    market adjustment through job creation may in any case often be desirable. It may be

    achieved by more flexible wages also increasing workers mobility and, equally

    important, a higher wage elasticity of jobs. In this context, it is also worth mentioning

    the role of regional policies and a banking union in Europe. Regional policies may be

    used to encourage job-creation in depressed regions, for example by offering tax

    deductions to firms moving in. In addition, the implementation of a banking union in

    Europe will foster adjustment through job creation. Morgan et al. (2004) show that

    increased interstate banking in the US stabilised fluctuations within states and reduced

    divergence between them.



    I. Figures

    Employment Growth

    Unemployment Rate

    Participation Rate

    Figure 1. Means of original variables

    Note: We plot the means of all European and the means of all US

    regions over time.

    Source: Labour Force Surveys with modification by authors for Europe

    and CPS for the US.




































    Europe US






































































    Figure 2. Standard deviation of

    regional unemployment rates

    Note: Standard deviations of unemployment rates

    shown in the middle panel of Figure 1.

    Source: Authors calculations.





































    Europe US



    Years 1 2 3 4 5 10

    1 2 3 4 5 10

    Employment 100 83 73 64 59 50

    100 92 72 62 59 57

    Employment rate 20 18 16 11 7 1

    13 9 5 1 0 0

    Participation rate 41 18 15 11 8 2

    44 30 12 4 2 0

    Migration 39 47 42 43 44 47

    43 54 56 57 57 57

    Figure 3. Adjustment to region-specific shocks

    Note: We plot the impulse responses to a one standard deviation shock to labour demand. The y-axis shows the effect of the shock

    in percentage deviations from steady-state and the x-axis shows years. We allow for two lags and estimate the model with least-

    squares. The grey area shows confidence bands of 95% bootstrapped with 250 replications. The table normalizes the size of the

    employment change to 100 and decomposes the employment response into contributions of the employment rate, the participation

    rate and migration, which is the unexplained part of the employment change. Source: Authors calculations.



    Years 1 2 3 4 5 20

    Employment 100 115 109 99 89 40

    Employment rate 42 52 45 35 26 -2

    Participation rate 40 41 37 33 28 0

    Migration 18 22 27 31 34 43

    Figure 4. National adjustment to country-specific shock

    Note: As Figure 3 but here we use the country factors and the small countries and allow for only one lag. Source: Authors calculations.








    1 2 3 4 5

    National Adjustment in Europe

    Regional Adjustment in Europe

    Regional Adjustment in US






    Employment Rate

    Participation Rate


    Regional Adjustment in Europe

    National Adjustment in Europe

    Regional Adjustment in the US

    Figure 5. Changes of migration

    Note: The left panel plots the number of migrants after a positive shock of 100 new jobs in the first

    five years. The right panel shows the average percentage contributions of the employment rate, the

    participation rate and migration to the employment change in the first three years. Note that these

    three variables together explain the total employment change.

    Source: Authors calculations.




    1 2 3 4 5








    24 60






    1 2 3 4 5



    30 41






    1 2 3 4 5


    31 59


    38 51





    Years 1 2 3 4 5 20

    1 2 3 4 5 20

    Employment 100 120 127 124 116 56

    100 108 99 93 88 63

    Employment rate 34 47 50 46 39 -5

    16 16 11 7 4 0

    Participation rate 41 38 41 39 36 15

    47 45 32 27 22 0

    Migration 25 35 36 39 41 46

    37 48 56 59 61 63

    Figure 6. Regional adjustment with simple differences

    Note: As Figure 3 but here we estimate the VAR in simple differences as in Blanchard and Katz (1992).

    Source: Authors calculations.

    Europe with simple differences

    US with simple differences

    Figure 7. Changes of migration with simple differences

    Note: As Figure 5.

    Source: Authors calculations.




    1 2 3 4 5


    1990-2013 27











    Employment Rate

    Participation Rate





    1 2 3 4 5









    II. Tables

    Figure 8. US regional adjustment with simple

    differences and LAUS data

    Note: As Figure 3 but with simple differences and LAUS data.

    Source: Authors calculations.

    Table 1. Variance Decomposition

    EU Country Region

    Employment Growth 28 29 43

    Employment Rate 41 36 23

    Participation Rate 69 16 16

    US Area State

    Employment Growth 41 15 44

    Employment Rate 71 17 12

    Participation Rate 60 19 21

    Note: The squared loading of a variable on a factor measures the

    explained variance by that factor. We report the explained variance

    for each variable in Europe and the US by aggregating over the area

    and country factors.

    Source: Authors calculations.



    Appendix A Regions



    Bayern Hessen

    Nieders. & Bremen

    Nord.-Westfalen R.-Pfalz & Saarl.

    S.Holst. & Hamb.


    Bassin Parisien

    Centre-Est Est

    Ile de France

    Mediterrane Nord-Pas-de-Cal.





    Campania Centro


    Lazio Lombardia


    Nord-Ovest Sardegna





    Centro Este


    Noreste Noroeste


    United Kingdom

    East Midlands

    East of England Northern Ireland


    South-West Wales

    West Midlands

    York and Humb.

    US Northeast



    Massachusetts New Hampshire

    New Jersey

    New York Pennsylvania

    Rhode Island


    US Midwest



    Iowa Kansas


    Minnesota Missouri


    North Dakota Ohio

    South Dakota


    US South



    DC Delaware


    Georgia Kentucky


    Maryland Mississippi

    North Carolina

    Oklahoma South Carolina


    Texas Virginia

    West Virginia

    US West



    California Colorado


    Idaho Montana


    New Mexico Oregon


    Washington Wyoming




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